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<Paper uid="W01-1411">
  <Title>Towards a Simple and Accurate Statistical Approach to Learning Translation Relationships among Words</Title>
  <Section position="11" start_page="7" end_page="7" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have evaluated our approach and found it to be comparable in accuracy on single-word translations to Melamed's results (which appear to be the best previous results, as far as one can tell given the lack of standard test corpora) up to nearly 60% type coverage and 97% token coverage. Space does not permit a detailed comparison of Melamed's methods to ours, but we repeat that ours are far simpler to implement and much faster to run. Our approach to generating translations involving muti-word compounds performs less well in general, but the special-case modification of it to deal with captoids performs with very high accuracy for those captoids it is able to find a translation for. Based on these results, the focus of our future work will be to try to extend our region of high-accuracy single-word translation to higher levels of coverage, improve the accuracy of our general method for finding multiword translations, and extend the coverage of our method for translating captoids.</Paragraph>
  </Section>
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